Introduction

Company earnings are the bedrock of financial analysis and investment. Sell-side, buy-side and independent research analysts perform quantitative and qualitative analysis of companies, their peers and their markets in order to provide guidance for short-term earnings and earnings growth for in-house use or for clients. Innovations in earnings analysis over the last few years have included crowd-sourced earnings estimates (e.g. Estimize) and sentiment derived from the news and social media (e.g. Ravenpack, Social Market Analytics, PsychSignal). The overriding objective of company analysis has been and still is to forecast as accurately as possible a company’s future earnings and so guide asset allocation and trading decisions.

As a software and services firm focused on quantitative research and trading solutions, we are always interested in alternative ways to apply quantitative techniques for trading and investment purposes. This fall, we decided to look at company earnings from a different perspective. This time, we looked at how calendar events affect forecasts: specifically, we wanted to test whether earnings announcement date revisions can be used for predicting future prices in a manner that could be profitably traded upon.

Research Methodology

We conducted our study using our own research software, TimeBase and QuantOffice. Below are the steps we followed for this research:

We built a data loader to populate TimeBase with the WSH daily snapshots of company future earnings announcement dates for S&P500 stocks for the period January 3, 2006 to September 2, 2015.

We also populated TimeBase with market data for those stocks for the same period. In an actual production deployment of Deltix software, a time series of tick data is automatically recorded through operation of the software for trading. For our research study, we back-populated TimeBase with one-minute bar data.

We now had a base data set on which to apply and test our ideas. Quant researchers use Deltix to express their model ideas as “strategies” in QuantOffice. In the studies by Joshua Livnat and Eric So referenced above, both found that companies who advance their earnings dates generally outperform companies that delay their earnings dates. It is not difficult to rationalize why this might hold true and so we started with this premise and then developed the theme with advanced statistical techniques implemented in QuantOffice.

The resulting model was back-tested as a trading strategy. (In practice, there were of course multiple iterations of the model with each backtest providing our researchers feedback for the next iteration. As each back-test takes seconds to run: the productivity of this iterative approach is very high).

Where both earlier studies modelled a holding period spanning from shortly after the change in date to the actual announcement, we took positions the day before an earnings announcement and sold them the day after, resulting on a holding period of less than 24 hours.

In order to isolate the calendar date effect from any general market effect (although our holding period was less than a day), we also implemented a dollar-neutral version of the strategy.

Results

Our results supported the findings of the previous researchers. Specifically, we found:

The most likely positive returns occured when the earnings announcement date was advanced (i.e. brought forward) in the second half of the quarter.

Conversely, the most probable negative returns occurred when earnings announcement date was delayed in the first half of the quarter.

For both hedged and un-hedged versions of the strategy, for the period January 2006 to September 2015, the back-tested strategies showed Sharpe Ratios of 2.08 (unhedged) and 2.12 (hedged) with average profit per share of 10 cents and 8 cents respectively.

As such, we can conclude that there are profitable opportunities from trading with signals derived from WSH earnings date announcement data.

The P&L curve for the unhedged version of the strategy is shown below:

Practical Applications – Research

One of the impediments to firms seeking alpha in alternative data sources is the time required to get to the point where consideration of the value of such sources can even start. Data is usually delivered as a real-time time stream or, for sample or trial purposes, a flat file. In either case, the raw data has to be populated into an analytical platform duly configured for the data.

By building a set of data loaders (real-time and batch) for our TimeBase data repository, Deltix significantly reduces the time required for setup before analysts can start their research. In addition, QuantOffice has a rich set of analytical libraries which enable quant researchers to focus on implementing their specific logic, rather than having to spend time on the time-series analytics required for such an implementation.

The Deltix Quantitative Research Team invested substantial time to prepare the data loaders and repository for this research study, and it is now available in the Deltix Product Suite. Researchers can access a paid trial in which they can access the data and QuantOffice strategy and modify it for their own purposes.

Practical Applications – Trading

Both Livnat and So generated long and short positions from soon after the date of the revision to just after the actual earnings announcement. Both found that positive returns can be generated from taking long and short positions, but both also found that the majority of returns generated from taking positions in advance of the actual earnings occurred on or around the actual EPS announcement. The strategy suggested in our research therefore focused on taking positions just before to just after the earnings announcement (holding period less of than 24 hours). This short-term holding period suggests that institutional managers could implement tactical trading around core positions in a portfolio in addition to outright portfolio construction.

About DELTIX

Deltix provides software and services to buy-side and sell-side firms for quantitative research and algorithmic trading. We cover data collection and aggregation, advanced analytics, model development, back-testing, simulation and live trading.